Siddhartha Banerjee

ORIE 4520 - Stochastics at Scale

Course Description

We will study a collection of interesting stochastic algorithms and models, that serve to illustrate the following idea:

Size is a critical consideration in the design of useful stochastic models and algorithms.

A formal way to express this is through the notion of scaling - given a model/algorithm for some system, how does it behave when we grow some aspect of the system. This course will try to build intuition behind the importance of scaling by presenting examples where understanding scaling is crucial for good system design.

Course Information

Course Material

There is no required textbook for the course. Different topics will be covered from different sources, and notes and links to the relevant material will be periodically posted here. In particular, the course will cover selected topics from the following textbooks:

Prerequisites

Knowledge of basic probability (at the level of ORIE 3500): in particular, random variables, conditional probability and expectation, common probability distributions and their properties (binomial, geometric, exponential, Poisson, Gaussian). The latter part of the course (after the prelim) will require knowledge of stochastic processes, in particular, Markov chains (at the level of ORIE 3510). There will be a recitation session covering the essentials, and students may be able to manage without the required background. Prior exposure to algorithms and graph theory will also be useful, but not essential.
Send a mail to the instructor if you are concerned about having the appropriate prerequisites.